Processing large datasets or working with huge data is a usual stage in almost any kind of research. The problem appears when processing these data requires long processing time on any kind of personal laptop. In order to solve that issue, we have at our hands the possibility of using any major compute cloud provider in order to accelerate that processing stage wasting minimal time and resources. Cloud computing allows us with a few clicks to build a computer cluster, process our data, get the results and destroy that cluster for only a few dollars.
Agile methods on software development are very popular. The root of these methods must be sought in the on the Agile Manifesto that says:
In short, these lines says that the world is always changing, the adaptation to change, the collaboration between people and the transfer of knowledge should be a priority in our professional life.
These objectives fit with software development but also with research because, we software developers and researchers are always looking for new challenges, always updating our professional network and making improvement in science.
In the last months, I conducted a few usability studies and upon reflecting on these I decided to share my experience as it might be helpful to anyone starting on usability. This article attemps at summarizing my experience and thoughts on usability experiments.
When trying to start a usability study or experiment, the practitioner or researcher must answer some initial questions about their future work.
Regarding your research, in general, the most important question to answer is “What is my motivation or why I am doing it?”. In a few words, as a researcher, you must not only formulate your research question but also, its answer.
Research methods are here to help you create and solve a new question on usability, user experience and also, on human-computer interaction.
This entry tries to be a very short guide on how to perform field experiments for usability and user experience. Fields experiments is reported to have many advantages over laboratory experiments as can be read in the HCI literature . What we try to obtain with field experiments is to overcome the complexity that real contexts represents and cannot be reproduced in a laboratory. As the literature also said, these experiments cannot be replaced by expert evaluations  because field experiments focus on the participants and the context: using real users in real context: the weather, user profiles, effectiveness of the locations-based systems, screen resolutions, keyboards… The only way to see how the user and the system performs is taking a ride and practice. As Nielsen  and Brewster  say, field experiments are always difficult to perform due to the problem that sufficient data must be acquired without interfering in the experiment neither conditioning the participants. Talking about mobile devices in general, its usability is an special concern because of the context and the environment the devices may be used. There are a lot of services or functionalities that depends on the context like location-based services and applications in outdoors which are difficult to simulate in a laboratory. So usability testing in the laboratory will be very limited and will never simulated a fully user case when testing usability in real context with real users.
Long, long time ago … I started with Octave and Matlab.They were amazing and allowed me to solve a lot of interesting problems in my research. I loved the command window of Octave, but I needed the productivity an IDE gives when developing complex calculations. None of the available IDE’s for Octave were not as powerful as the Matlab IDE. The problem was that Matlab was not GNU and buying a license was very expensive. Then, I found R and I realized that none Octave neither Matlab were the tool I needed for my research. I needed advanced project and file management through repositories, fast data manipulation, an easy way to export my calculations, a creative way of authoring reports and a powerful IDE that let me access my beloved command window. Now R gives me all I need and is an important part of my everyday toolbox. For those who does not known R, I must say that R is a well known programming language that is widely used on mathematics, economy, biology… Its main benefits includes the ability to work easily with statistics and data manipulation. R is very popular on academics and research, is GNU, very powerful and have a lot of packages that allows do magical things in a few clicks or with a few commands.